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Search Publications by: Carelyn E. Campbell (Fed)

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Displaying 1 - 25 of 122

Structure-Aware GNN-Based Deep Transfer Learning Framework For Enhanced Predictive Analytics On Small Materials Data

January 2, 2024
Author(s)
Vishu Gupta, Kamal Choudhary, Brian DeCost, Francesca Tavazza, Carelyn E. Campbell, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
Modern data mining methods have been demonstrated to be effective tools to comprehend and predict materials properties. An essential component in the process of materials discovery is to know which material(s) (represented by their composition and crystal

Applying the Effective Bond Energy Formalism (EBEF) to Describe the Sigma (s) Phase in the Co-Cr-Ni-Re System

December 30, 2023
Author(s)
Julio Cesar Pereira Dos Santos, Sean Griesemer, Nathalie Dupin, Ursula R. Kattner, Chuan Liu, Daniela Ivanova, Thomas Hammerschmidt, Suzana Fries, Chris Wolverton, Carelyn E. Campbell
Proper descriptions of Topologically Closed-Packed (TCP) phases in thermodynamic databases are essential to adequately design new alloys. Thus, the recently introduced Effective Bond Energy Formalism (EBEF) is used in this work to describe the sigma (σ)

MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction

March 27, 2023
Author(s)
Kamal Choudhary, Francesca Tavazza, Carelyn E. Campbell, Vishu Gupta, Yuwei Mao, Kewei Wang, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from available

Developing an Appropriate Heat Treatment Protocol for Additively Manufactured Alloy 718 for Oil and Gas Applications

March 23, 2023
Author(s)
Mark R. Stoudt, James Zuback, Carelyn E. Campbell, Maureen E. Williams, Kil-Won Moon, Carlos R. Beauchamp, Mark Yunovich
The combination of strength, corrosion resistance, and excellent weldability makes Alloy 718 an attractive alloy for additive manufacturing (AM) applications, but the AM build process generates considerable residual stresses, and large compositional and

Development of a Diffusion Mobility Database for Co-based Superalloys

November 27, 2022
Author(s)
Greta Lindwall, Kil-Won Moon, Carelyn E. Campbell, Maureen E. Williams, Whitney Tso
To facilitate the development of high-temperature Co-based - superalloys, a Co-Ni based diffusion mobility database is developed for the eight component FCC (Face Centered Cubic) system of Co-Al-W-Ni-Cr-Ti-Ta-Re. A CALPHAD approach is used to represent

The effects of diffusional couplings on compositional trajectories and interfacial free energies during phase separation in a quaternary Ni-Al-Cr-Re model superalloy

August 1, 2022
Author(s)
Carelyn E. Campbell, Sung-Il Baik, Zugang Mao, Qingqiang Ren, Chuan Zhang, BiCheng Zhou, Ronald D. Noebe, David N. Seidman, Fei Xue
The temporal evolution of ordered γ′(L12)-precipitates and the compositional trajectories during phase-separation of the γ(face-centered-cubic (f.c.c.))- and γ′(L12)-phases are studied in a Ni–0.10Al-0.085Cr-0.02Re (mole-fraction) superalloy, utilizing

Co-Based Superalloy Morphology Evolution: A Phase Field Study Based on Experimental Thermodynamic and Kinetic Data

July 1, 2022
Author(s)
Carelyn E. Campbell, Ursula R. Kattner, Jonathan E. Guyer, James A. Warren, Wenkun Wu, Peter Voorhees, Olle Heinonen
Cobalt-based superalloys with gamma/gamma prime microstructures off er great promise as candidates for next-generation high-temperature alloys for applications, such as turbine blades. It is essential to understand the thermodynamic and kinetic factors

A Roadmap for LIMS at NIST Material Measurement Laboratory

April 11, 2022
Author(s)
Gretchen Greene, Jared Ragland, Zachary Trautt, June W. Lau, Raymond Plante, Joshua Taillon, Adam Abel Creuziger, Chandler A. Becker, Joe Bennett, Niksa Blonder, Lisa Borsuk, Carelyn E. Campbell, Adam Friss, Lucas Hale, Michael Halter, Robert Hanisch, Gary R. Hardin, Lyle E. Levine, Samantha Maragh, Sierra Miller, Chris Muzny, Marcus William Newrock, John Perkins, Anne L. Plant, Bruce D. Ravel, David J. Ross, John Henry J. Scott, Christopher Szakal, Alessandro Tona, Peter Vallone
Instrumentation generates data faster and in higher quantity than ever before, and interlaboratory research is in historic demand domestically and internationally to stimulate economic innovation. Strategic mission needs of the NIST Material Measurement

How Austenitic is a Martensitic Steel Produced by Laser Powder Bed Fusion? A Cautionary Tale

December 2, 2021
Author(s)
Fan Zhang, Mark R. Stoudt, Souzan Hammadi, Carelyn E. Campbell, Eric A. Lass, Maureen E. Williams
Accurate phase fraction analysis is an essential element of microstructural characterization of alloys and often serves as a basis to quantify effects such as heat treatment or mechanical deformation. Additive manufacturing (AM) of metals, due to the

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

November 15, 2021
Author(s)
Vishu Gupta, Kamal Choudhary, Francesca Tavazza, Carelyn E. Campbell, Wei-Keng Liao, Alok Choudhary, Ankit Agrawal
Artificial Intelligence (AI) and Machine Learning (ML) has been increasingly used in materials science to build property prediction models and accelerate materials discovery. The availability of large materials databases for some properties like formation

Development of Computational Framework for Titanium Alloy Phase Transformation Prediction in Laser Powder-bed Direct Energy Additive Manufacturing

October 16, 2020
Author(s)
Zhi Liang, Ivan Zhirnov, Fan Zhang, Kevontrez K. Jones, David C. Deisenroth, Maureen E. Williams, Ursula R. Kattner, Kil-Won Moon, Wing-Kam Liu, Brandon M. Lane, Carelyn E. Campbell
In conjunction with bare metal single laser track validation experiments, a computational framework is proposed to accelerate the design and development of new additive manufacturing (AM) specific alloys. Specifically, Additive Manufacturing-Computational